PROJECT TITLE: ANALYSIS OF THE VARIATIONS IN CONTENTS AND PROPERTIES OF CEREALS

This project analyzes the contents of different cereals ranging from their carbo, vitamin, sugar, pottassium contents

OBJECTIVES;
1. Clean and thouroughly analyze the dataset 
2. Analyze some important trends in the dataset.
3. Answer data driven questions and showcase answers using charts.

#import libraries
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go

#import dataset
data=pd.read_csv(r"C:\Users\AISHAT\Desktop\PROJECTS\Cognorise\archive (1)\cereal.csv")
data.head()

	name	mfr	type	calories	protein	fat	sodium	fiber	carbo	sugars	potass	vitamins	shelf	weight	cups	rating
0	100% Bran	N	C	70	4	1	130	10.0	5.0	6	280	25	3	1.0	0.33	68.40
1	100% Natural Bran	Q	C	120	3	5	15	2.0	8.0	8	135	0	3	1.0	1.00	33.98
2	All-Bran	K	C	70	4	1	260	9.0	7.0	5	320	25	3	1.0	0.33	59.43
3	All-Bran with Extra Fiber	K	C	50	4	0	140	14.0	8.0	0	330	25	3	1.0	0.50	93.70
4	Almond Delight	R	C	110	2	2	200	1.0	14.0	8	-1	25	3	1.0	0.75	34.38

DATA CLEANING

#checking for null values
data.isnull().sum()

name        0
mfr         0
type        0
calories    0
protein     0
fat         0
sodium      0
fiber       0
carbo       0
sugars      0
potass      0
vitamins    0
shelf       0
weight      0
cups        0
rating      0
dtype: int64

data.duplicated().sum()

0

EXPLORATION

data.shape

(77, 16)

data.dtypes

name         object
mfr          object
type         object
calories      int64
protein       int64
fat           int64
sodium        int64
fiber       float64
carbo       float64
sugars        int64
potass        int64
vitamins      int64
shelf         int64
weight      float64
cups        float64
rating      float64
dtype: object

data.describe()

	calories	protein	fat	sodium	fiber	carbo	sugars	potass	vitamins	shelf	weight	cups	rating
count	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000
mean	106.883117	2.545455	1.012987	159.675325	2.151948	14.597403	6.922078	96.077922	28.246753	2.207792	1.029610	0.821039	42.665325
std	19.484119	1.094790	1.006473	83.832295	2.383364	4.278956	4.444885	71.286813	22.342523	0.832524	0.150477	0.232716	14.047301
min	50.000000	1.000000	0.000000	0.000000	0.000000	-1.000000	-1.000000	-1.000000	0.000000	1.000000	0.500000	0.250000	18.040000
25%	100.000000	2.000000	0.000000	130.000000	1.000000	12.000000	3.000000	40.000000	25.000000	1.000000	1.000000	0.670000	33.170000
50%	110.000000	3.000000	1.000000	180.000000	2.000000	14.000000	7.000000	90.000000	25.000000	2.000000	1.000000	0.750000	40.400000
75%	110.000000	3.000000	2.000000	210.000000	3.000000	17.000000	11.000000	120.000000	25.000000	3.000000	1.000000	1.000000	50.830000
max	160.000000	6.000000	5.000000	320.000000	14.000000	23.000000	15.000000	330.000000	100.000000	3.000000	1.500000	1.500000	93.700000

ANALYSIS

#products and their calories
figure=px.scatter(data_frame=data, x="name", y="calories", color="name", title="calories")
figure.show()

#products with the highest calories
cal=data.sort_values(by=["calories"], ascending=False).head(10)
cal

	name	mfr	type	calories	protein	fat	sodium	fiber	carbo	sugars	potass	vitamins	shelf	weight	cups	rating
46	Mueslix Crispy Blend	K	C	160	3	2	150	3.0	17.0	13	160	25	3	1.50	0.67	30.31
44	Muesli Raisins; Dates; & Almonds	R	C	150	4	3	95	3.0	16.0	11	170	25	3	1.00	1.00	37.14
45	Muesli Raisins; Peaches; & Pecans	R	C	150	4	3	150	3.0	16.0	11	170	25	3	1.00	1.00	34.14
70	Total Raisin Bran	G	C	140	3	1	190	4.0	15.0	14	230	100	3	1.50	1.00	28.59
39	Just Right Fruit & Nut	K	C	140	3	1	170	2.0	20.0	9	95	100	3	1.30	0.75	36.47
49	Nutri-Grain Almond-Raisin	K	C	140	3	2	220	3.0	21.0	7	130	25	3	1.33	0.67	40.69
51	Oatmeal Raisin Crisp	G	C	130	3	2	170	1.5	13.5	10	120	25	3	1.25	0.50	30.45
7	Basic 4	G	C	130	3	2	210	2.0	18.0	8	100	25	3	1.33	0.75	37.04
52	Post Nat. Raisin Bran	P	C	120	3	1	200	6.0	11.0	14	260	25	3	1.33	0.67	37.84
35	Honey Graham Ohs	Q	C	120	1	2	220	1.0	12.0	11	45	25	2	1.00	1.00	21.87

#chat of products with the highest calories
figure=px.bar(data_frame=cal, x="name", y="calories", title="products with the highest calories", color="mfr")
figure.show()

#products with the lowest calories
l_cal=data.sort_values(by=["calories"], ascending=True).head(10)
figure=px.bar(data_frame=l_cal, x="name", y="calories", title="products with the lowest calories", color="mfr")
figure.show()

#products with the highest rating
rate=data.sort_values(by=["rating"], ascending=False).head(10)
rate

	name	mfr	type	calories	protein	fat	sodium	fiber	carbo	sugars	potass	vitamins	shelf	weight	cups	rating
3	All-Bran with Extra Fiber	K	C	50	4	0	140	14.0	8.0	0	330	25	3	1.00	0.50	93.70
64	Shredded Wheat 'n'Bran	N	C	90	3	0	0	4.0	19.0	0	140	0	1	1.00	0.67	74.47
65	Shredded Wheat spoon size	N	C	90	3	0	0	3.0	20.0	0	120	0	1	1.00	0.67	72.80
0	100% Bran	N	C	70	4	1	130	10.0	5.0	6	280	25	3	1.00	0.33	68.40
63	Shredded Wheat	N	C	80	2	0	0	3.0	16.0	0	95	0	1	0.83	1.00	68.24
20	Cream of Wheat (Quick)	N	H	100	3	0	80	1.0	21.0	0	-1	0	2	1.00	1.00	64.53
55	Puffed Wheat	Q	C	50	2	0	0	1.0	10.0	0	50	0	3	0.50	1.00	63.01
54	Puffed Rice	Q	C	50	1	0	0	0.0	13.0	0	15	0	3	0.50	1.00	60.76
50	Nutri-grain Wheat	K	C	90	3	0	170	3.0	18.0	2	90	25	3	1.00	1.00	59.64
2	All-Bran	K	C	70	4	1	260	9.0	7.0	5	320	25	3	1.00	0.33	59.43

#chat of products with the highest rating
figure=px.bar(data_frame=rate, x="name", y="rating", title="products with the highest ratings", color="mfr")
figure.show()

#products with the lowest rating
l_rate=data.sort_values(by=["rating"], ascending=True).head(10)
figure=px.bar(data_frame=l_rate, x="name", y="rating", title="products with the lowest ratings", color="mfr")
figure.show()

figure=px.histogram(data_frame=data, x="rating", title="count of ratings")
figure.show()

#products with their weights
figure=px.pie(data_frame=data, names="weight", color="name", title="%weights")
figure.show()